Edge Detection with Sub-pixel Accuracy Based on Approximation of Edge with Erf Function

Edge detection is an often used procedure in digital image processing. For some practical applications is desirable to detect edges with sub-pixel accuracy. In this paper we present edge detection method for 1-D images based on approximation of real image function with Erf function. This method is verified by simulations and experiments for various numbers of samples of simulated and real images. Results of simulations and experiments are also used to compare proposed edge detection scheme with two often used moment-based edge detectors with sub-pixel precision

An edge-directed interpolation method for fetal spine MR images

Abstract Background Fetal spinal magnetic resonance imaging (MRI) is a prenatal routine for proper assessment of fetus development, especially when suspected spinal malformations occur while ultrasound fails to provide details. Limited by hardware, fetal spine MR images suffer from its low resolution. High-resolution MR images can directly enhance readability and improve diagnosis accuracy. Image interpolation for higher resolution is required in clinical situations, while many methods fail to preserve edge structures. Edge carries heavy structural messages of objects in visual scenes for doctors to detect suspicions, classify malformations and make correct diagnosis. Effective interpolation with well-preserved edge structures is still challenging. Method In this paper, we propose an edge-directed interpolation (EDI) method and apply it on a group of fetal spine MR images to evaluate its feasibility and performance. This method takes edge messages from Canny edge detector to guide further pixel modification. First, low-resolution (LR) images of fetal spine are interpolated into high-resolution (HR) images with targeted factor by bi-linear method. Then edge information from LR and HR images is put into a twofold strategy to sharpen or soften edge structures. Finally a HR image with well-preserved edge structures is generated. The HR images obtained from proposed method are validated and compared with that from other four EDI methods. Performances are evaluated from six metrics, and subjective analysis of visual quality is based on regions of interest (ROI). Results All these five EDI methods are able to generate HR images with enriched details. From quantitative analysis of six metrics, the proposed method outperforms the other four from signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM) and mutual information (MI) with seconds-level time consumptions (TC). Visual analysis of ROI shows that the proposed method maintains better consistency in edge structures with the original images. Conclusions The proposed method classifies edge orientations into four categories and well preserves structures. It generates convincing HR images with fine details and is suitable in real-time situations. Iterative curvature-based interpolation (ICBI) method may result in crisper edges, while the other three methods are sensitive to noise and artifacts

Image understanding and feature extraction for applications in industry and mapping

Bibliography: p. 212-220.The aim of digital photogrammetry is the automated extraction and classification of the three dimensional information of a scene from a number of images. Existing photogrammetric systems are semi-automatic requiring manual editing and control, and have very limited domains of application so that image understanding capabilities are left to the user. Among the most important steps in a fully integrated system are the extraction of features suitable for matching, the establishment of the correspondence between matching points and object classification. The following study attempts to explore the applicability of pattern recognition concepts in conjunction with existing area-based methods, feature-based techniques and other approaches used in computer vision in order to increase the level of automation and as a general alternative and addition to existing methods. As an illustration of the pattern recognition approach examples of industrial applications are given. The underlying method is then extended to the identification of objects in aerial images of urban scenes and to the location of targets in close-range photogrammetric applications. Various moment-based techniques are considered as pattern classifiers including geometric invariant moments, Legendre moments, Zernike moments and pseudo-Zernike moments. Two-dimensional Fourier transforms are also considered as pattern classifiers. The suitability of these techniques is assessed. These are then applied as object locators and as feature extractors or interest operators. Additionally the use of fractal dimension to segment natural scenes for regional classification in order to limit the search space for particular objects is considered. The pattern recognition techniques require considerable preprocessing of images. The various image processing techniques required are explained where needed. Extracted feature points are matched using relaxation based techniques in conjunction with area-based methods to 'obtain subpixel accuracy. A subpixel pattern recognition based method is also proposed and an investigation into improved area-based subpixel matching methods is undertaken. An algorithm for determining relative orientation parameters incorporating the epipolar line constraint is investigated and compared with a standard relative orientation algorithm. In conclusion a basic system that can be automated based on some novel techniques in conjunction with existing methods is described and implemented in a mapping application. This system could be largely automated with suitably powerful computers

Computer vision and optimization methods applied to the measurements of in-plane deformations

fi=vertaisarvioitu|en=peerReviewed

A fruit recognition method for automatic harvesting

Automation of harvesting is always one of the hottest topics in greenhouse operation. But before this, a reliable method of identifying mature fruit clusters on plants is required. This thesis presents a method to detect and recognize mature tomato fruit clusters on a complex-structured tomato plant containing clutter and occlusion in a tomato greenhouse. A color stereo vision camera is applied as the vision sensor. The proposed method performs a 3D reconstruction with the data collected by the stereo camera to create a 3D environment for further processing. The Color Layer Growing (CLG) method is introduced to segment the mature fruits from the leaves, stalks, background and noise. Target fruit clusters can then be located by depth segmentation. The experimental data was collected from a tomato greenhouse and the method is justified by the experimental results

Image Transition Edge Physical Modeling Method for Exact Object Shape Position Determination

Disertační práce se zabývá návrhem nové originální metody fyzikálního modelování přechodových hran v obraze pro určení skutečné subpixelové pozice obrysu předmětu. Aplikační oblastí vyvinuté metody je oblast vysoce přesných měření geometrických rozměrů předmětů v průmyslových inspekčních systémech. Návrh metody vychází z detailní fyzikální analýzy definovaného zobrazovacího systému, jehož stěžejními částmi jsou telecentrická optická soustava, zdroj vysoce telecentrického osvětlení a CCD kamera. Výsledkem provedené fyzikální analýzy z pohledu geometrické a Fourierovské optiky je komplexní fyzikální model zobrazení hrany předmětu zájmu, spolu s definicí způsobu určení skutečné pozice obrysu předmětu v rámci modelu. Model je podrobně prozkoumán a diskutován z hlediska vlivu jednotlivých parametrů na přesnost určení pozice obrysu předmětu. Následně je z něj odvozena modelující funkce přechodové hrany v podobě vhodné pro definici metody optimalizované aproximace reálných přechodových hran. Navržená metoda fyzikálního modelování přechodové hrany a určení subpixelové pozice obrysu předmětu je nejprve ověřena se syntetickými daty a implementována do měřicího systému Tester2D. Metodou dosahovaná přesnost měření geometrických rozměrů z reálně snímaných obrazů je ověřena formou měření etalonů délky. Měřicí systém Tester2D byl úspěšně akreditován pro měření rozměrů v rozsahu s přesností až , což je doloženo protokolem o akreditaci v příloze disertační práce, spolu s dokladem o dosažených výsledcích měřicího systému v rámci uskutečněného mezilaboratorního srovnávání.Doctoral thesis is focused on a design of a new original image transition edge physical modeling method for exact object shape position determination. Automatic Optical Inspection systems for the high accuracy optical measurements is main application area for designed method. The new method design is based on precise physical analysis of a defined imaging system. Object side telecentric lens, telecentric backlight source and CCD video camera are main parts of the analyzed imaging system. New image transition edge physical model and method for accurate shape position detection within the model are derived by geometrical and Fourier optics imaging system analysis. Possible influences of the model parameters changes to the accuracy of shape position detection are studied precisely. A new modeling function suitable for implementation in a new optimal approximation method is derived from the physical transition edge model. The modeling function optimal approximation method is implemented in to a Tester2D measuring system and verified by length etalon measurements. The Tester2D measuring system was successfully accredited for dimensions measurement in range with accuracy up to . Documentation of results of the accreditation process with the record of obtained results from measurement system in scope of preformed interlaboratory comparison tests are appended to the doctoral thesis.

Video modeling via implicit motion representations

Video modeling refers to the development of analytical representations for explaining the intensity distribution in video signals. Based on the analytical representation, we can develop algorithms for accomplishing particular video-related tasks. Therefore video modeling provides us a foundation to bridge video data and related-tasks. Although there are many video models proposed in the past decades, the rise of new applications calls for more efficient and accurate video modeling approaches.;Most existing video modeling approaches are based on explicit motion representations, where motion information is explicitly expressed by correspondence-based representations (i.e., motion velocity or displacement). Although it is conceptually simple, the limitations of those representations and the suboptimum of motion estimation techniques can degrade such video modeling approaches, especially for handling complex motion or non-ideal observation video data. In this thesis, we propose to investigate video modeling without explicit motion representation. Motion information is implicitly embedded into the spatio-temporal dependency among pixels or patches instead of being explicitly described by motion vectors.;Firstly, we propose a parametric model based on a spatio-temporal adaptive localized learning (STALL). We formulate video modeling as a linear regression problem, in which motion information is embedded within the regression coefficients. The coefficients are adaptively learned within a local space-time window based on LMMSE criterion. Incorporating a spatio-temporal resampling and a Bayesian fusion scheme, we can enhance the modeling capability of STALL on more general videos. Under the framework of STALL, we can develop video processing algorithms for a variety of applications by adjusting model parameters (i.e., the size and topology of model support and training window). We apply STALL on three video processing problems. The simulation results show that motion information can be efficiently exploited by our implicit motion representation and the resampling and fusion do help to enhance the modeling capability of STALL.;Secondly, we propose a nonparametric video modeling approach, which is not dependent on explicit motion estimation. Assuming the video sequence is composed of many overlapping space-time patches, we propose to embed motion-related information into the relationships among video patches and develop a generic sparsity-based prior for typical video sequences. First, we extend block matching to more general kNN-based patch clustering, which provides an implicit and distributed representation for motion information. We propose to enforce the sparsity constraint on a higher-dimensional data array signal, which is generated by packing the patches in the similar patch set. Then we solve the inference problem by updating the kNN array and the wanted signal iteratively. Finally, we present a Bayesian fusion approach to fuse multiple-hypothesis inferences. Simulation results in video error concealment, denoising, and deartifacting are reported to demonstrate its modeling capability.;Finally, we summarize the proposed two video modeling approaches. We also point out the perspectives of implicit motion representations in applications ranging from low to high level problems

Development Of A High Performance Mosaicing And Super-Resolution Algorithm

In this dissertation, a high-performance mosaicing and super-resolution algorithm is described. The scale invariant feature transform (SIFT)-based mosaicing algorithm builds an initial mosaic which is iteratively updated by the robust super resolution algorithm to achieve the final high-resolution mosaic. Two different types of datasets are used for testing: high altitude balloon data and unmanned aerial vehicle data. To evaluate our algorithm, five performance metrics are employed: mean square error, peak signal to noise ratio, singular value decomposition, slope of reciprocal singular value curve, and cumulative probability of blur detection. Extensive testing shows that the proposed algorithm is effective in improving the captured aerial data and the performance metrics are accurate in quantifying the evaluation of the algorithm